Journal of Drive and Control (드라이브 ㆍ 컨트롤)

The Korean Society for Fluid Power and Construction Equipment (유공압건설기계학회)
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Model-Prediction-based Collision-Avoidance Algorithm for Excavators Using the RLS Estimation of Rotational Inertia

회전관성의 순환최소자승 추정을 이용한 모델 예견 기반 굴삭기의 충돌회피 알고리즘 개발

  • Oh, Kwang Seok (Department of Automotive Engineering, Honam University) ;
  • Seo, Jaho (Department of Biosystems Machinery Engineering, Chungnam National University) ;
  • Lee, Geun Ho (Korea Institute of Machinery & Materials)
  • Received : 2016.08.05
  • Accepted : 2016.11.25
  • Published : 2016.12.01
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Abstract

This paper proposes a model-prediction-based collision-avoidance algorithm for excavators for which the recursive-least-squares (RLS) estimation of the excavator's rotational inertia is used. To estimate the rotational inertia of the excavator, the RLS estimation with multiple forgetting and two updating rules for the nominal parameter and the forgetting factors was conducted based on the excavator-swing dynamics. The average value of the estimated rotational inertia that is for the minimizing effects of the estimation error was computed using the recursive-average method with forgetting. Based on the swing dynamics, the computed average of the rotational inertia, the damping coefficient for braking, and the excavator's braking angle were predicted, and the predicted braking angle was compared with the detected-object angle for a safety evaluation. The safety level defined in this study consists of the three levels safe, warning, and emergency braking. The analytical rotational-inertia-based performance evaluation of the designed estimation algorithm was conducted using a typical working scenario. The results of the safety evaluation show that the predictive safety-evaluation algorithm of the proposed model can evaluate the safety level of the excavator during its operation.

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References

  1. J. Hinze and J. Teizer, "Visibility-related fatalities related to construction equipment," Safety Science, Vol. 49, pp. 709-718, 2011. https://doi.org/10.1016/j.ssci.2011.01.007
  2. U. Lee, J. Kim, H. Cho, and K. Kang, "Development of a mobile safety monitoring system for construction sites," Automation in Construction, Vol. 18, pp. 258-264, 2009. https://doi.org/10.1016/j.autcon.2008.08.002
  3. Y. Cho and M. Gai, "Projection-Recognition-Projection Method for automatic object recognition and registration for dynamic heavy equipment operations," Journal of Computing in Civil Engineering, Vol. 28, pp. 258-264, 2014.
  4. F. Vahdatikhaki and Amin Hammad, "Dynamic equipment workspace generation for improving earthwork safety using real-time location system," Advanced Engineering Informatics, Vol. 29, pp. 459-471, 2015. https://doi.org/10.1016/j.aei.2015.03.002
  5. S. Park, K. Oh, J. Park, J. Kim, J. Seo, G. Lee, and K. Yi, "Development of an environment monitoring technology of working site for construction machinery based on laser scanner," Transactions of the Korean Society of mechanical engineers, pp. 279-284, 2015.
  6. X. Xu, H. He, and D. Hu, "Efficient reinforcement learning using recursive least-squares methods," Journal of Artificial Intelligence Research, Vol. 16, pp. 259-292, 2002.
  7. B. Pence, H. Fathy, and J. Stein, "Recursive estimation for reduced-order state-space models using polynomial chaos theory applied to vehicle mass estimation," IEEE Transactions on Control System Technology, Vol. 22, pp. 224-229, 2014. https://doi.org/10.1109/TCST.2013.2252349
  8. K. Pettersson and Seppo Tikkanen, "Secondary control in construction machinery design and evaluation of an excavator swing drive," The 11th Scandinavian International Conference on Fluid Power, SICFP'09, June, Linkoping, Sweden, Vol. 9, pp. 2-4, 2009.

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  1. 적응형 슬라이딩 모드 제어를 이용한 위상 궤적 해석 기반 굴삭기의 안전제어 알고리즘 개발 vol.15, pp.3, 2016, https://doi.org/10.7839/ksfc.2018.15.3.008